Efficient Emitter Sampling for Spectral Path Tracing

Abstract

Spectral Monte-Carlo methods are powerful physically-based techniques for simulating wavelength-dependent phenomena such as dispersion. However, compared to tristimulus rendering, they involve sampling the spectral domain, which adds substantial overhead, requiring significantly more samples for noise-free, realistic-looking renders. Thereby, we propose a simple approach to efficiently sample emitters. We precompute a simple 2-dimensional data structure using spectral power distributions of scene emitters. We use it to model a probability distribution function to sample an emitter that yields high path throughput at every intersection when using Next Event Estimation. Our method handles various geometries and spectral distributions of scene emitters, improves convergence, and reduces noise with negligible overhead.